In this thesis, an attempt has been made to assist the diagnosis of Fungal Keratitis, a fungal infection that occurs in the corneal layers of the eye, by identifying the region of infection in the corneal images using fractal-based features. Three features related to the fractal dimension of the surface of the image, when represented in a 3D using the pixel intensity measure, are used to identify these regions in the image. To reduce the computation complexity, Fisher linear discriminant (FLD) is used to reduce the 3D raw feature to 1D feature, while preserving feature values. Using the adaptive mixtures (AM) method, the probability density distribution of the two class fractal features, is estimated. A training corneal image has been used to build the two-class probability density distribution. In this work, we use Bayesian classifier, a standard statistical pattern classification technique, to classify the pixels in corneal images, using the two-class probability density distribution. The classifier outputs an image mask, highlighting the fungal infected region in the corneal image. The whole system is implemented in MATLAB.